class_docstr: Construct an MBART tokenizer.
  
  Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
  [SentencePiece](https://github.com/google/sentencepiece).
  
  The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
  <tokens> <eos>` for target language documents.
  
  Examples:
  
  ```python
  >>> from transformers import MBartTokenizer
  
  >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO")
  >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
  >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
  >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
  ```
code: def prepare_seq2seq_batch(
      self,
      src_texts: List[str],
      src_lang: str = "en_XX",
      tgt_texts: Optional[List[str]] = None,
      tgt_lang: str = "ro_RO",
      **kwargs,
  ) -> BatchEncoding:
      self.src_lang = src_lang
      self.tgt_lang = tgt_lang
      return super().prepare_seq2seq_batch(
          src_texts, tgt_texts, **kwargs
      )
  
imports: import os
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 17 for calling the MBartTokenizer obj's underlying member method code with example usage: obj.prepare_seq2seq_batch(src_texts, src_lang, tgt_texts, tgt_lang) and returns: super